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Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx.

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

  • listing_id: unique identifier of listing
  • price: nightly listing price in USD
  • nbhood_full: name of borough and neighborhood where listing is located

data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

  • listing_id: unique identifier of listing
  • description: listing description
  • room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments

data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

  • listing_id: unique identifier of listing
  • host_name: name of listing host
  • last_review: date when the listing was last reviewed
# Import necessary packages
import pandas as pd
import numpy as np

# Begin coding here ...
# Use as many cells as you like

# Load CSV file
price_df = pd.read_csv("data/airbnb_price.csv")

# Load Excel file
room_df = pd.read_excel("data/airbnb_room_type.xlsx")

# Load TSV file
review_df = pd.read_csv("data/airbnb_last_review.tsv", sep='\t')

print(price_df.head(), price_df.shape)
print(room_df.head(), room_df.shape)
print(review_df.head(), review_df.shape)
print(price_df.isnull().sum())
print(room_df.isnull().sum())
print(review_df.isnull().sum())
# Convert last_review column to datetime
review_df['last_review'] = pd.to_datetime(review_df['last_review'], errors='coerce')

# Get earliest and latest review dates
first_reviewed = review_df['last_review'].min()
last_reviewed = review_df['last_review'].max()
# Standardize the room_type column to lowercase
room_df['room_type'] = room_df['room_type'].str.lower().str.strip()

# Filter for private rooms
private_room_df = room_df[room_df['room_type'] == 'private room']

# Count the number of private room listings
nb_private_rooms = len(private_room_df)
# Remove currency symbols, commas, and non-numeric characters, convert to float
price_df['price'] = price_df['price'].replace('[\$,]', '', regex=True)
price_df['price'] = price_df['price'].replace('[^\d.]', '', regex=True).astype(float)
avg_price = round(price_df['price'].mean(), 2)
# Combine into a DataFrame
review_dates = pd.DataFrame({
    'first_reviewed': [first_reviewed],
    'last_reviewed': [last_reviewed],
    'nb_private_rooms': [nb_private_rooms],
    'avg_price': [avg_price]
})
print(review_dates)